import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from matplotlib import pyplot as plt
from plotly.subplots import make_subplots
import plotly.graph_objects as go

data = pd.read_csv('C:/Users/jsree/OneDrive/Documents/Python projects/Kaggle/Diamond Price/diamonds.csv')

data.head()

	Unnamed: 0	carat	cut	color	clarity	depth	table	price	x	y	z
0	1	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	2	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	3	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	4	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	5	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

print(f'Data contain {data.shape[0]} diamonds and {data.shape[1]} columns.')

Data contain 53940 diamonds and 11 columns.

data.dtypes

Unnamed: 0      int64
carat         float64
cut            object
color          object
clarity        object
depth         float64
table         float64
price           int64
x             float64
y             float64
z             float64
dtype: object

data.describe().T

	count	mean	std	min	25%	50%	75%	max
Unnamed: 0	53940.0	26970.500000	15571.281097	1.0	13485.75	26970.50	40455.25	53940.00
carat	53940.0	0.797940	0.474011	0.2	0.40	0.70	1.04	5.01
depth	53940.0	61.749405	1.432621	43.0	61.00	61.80	62.50	79.00
table	53940.0	57.457184	2.234491	43.0	56.00	57.00	59.00	95.00
price	53940.0	3932.799722	3989.439738	326.0	950.00	2401.00	5324.25	18823.00
x	53940.0	5.731157	1.121761	0.0	4.71	5.70	6.54	10.74
y	53940.0	5.734526	1.142135	0.0	4.72	5.71	6.54	58.90
z	53940.0	3.538734	0.705699	0.0	2.91	3.53	4.04	31.80

count = data['cut'].value_counts()
count

Ideal        21551
Premium      13791
Very Good    12082
Good          4906
Fair          1610
Name: cut, dtype: int64

sns.displot(data=data, x=data["cut"])

<seaborn.axisgrid.FacetGrid at 0x233cf204370>

sns.barplot(x ="cut", y ="price", data = data)

<AxesSubplot:xlabel='cut', ylabel='price'>

data['price'].describe()

count    53940.000000
mean      3932.799722
std       3989.439738
min        326.000000
25%        950.000000
50%       2401.000000
75%       5324.250000
max      18823.000000
Name: price, dtype: float64

fig_6 = make_subplots(rows=1, cols=1, specs=[[{'type': 'xy'}]])
# Setting Box parameters
fig_6.add_trace(go.Box(x=data['price'], 
                       name='p'))

fig_6.update_traces(marker_color='salmon')

# Setting the parameters of the Box when displaying
fig_6.update_layout(showlegend=False, 
                    template='simple_white', 
                    font=dict(family='Arial', 
                              size=8, 
                              color='black'))

# Displaying the Box
fig_6.show()

The price of diamonds ranges between (326 −18.823k ) and its average is equal to 2401$

count = data['color'].value_counts()
count

G    11292
E     9797
F     9542
H     8304
D     6775
I     5422
J     2808
Name: color, dtype: int64

Display color column Color of Diamonds D, E, F -These are the whitest diamonds G, H – These stones have a very slight hue of yellow (or gray or brown). I, J – These stones have light to medium hue of yellow (or gray or brown). K, L – These stones have a strong hue of yellow (or gray or brown). M, N – These stones have a very strong hue of yellow (or gray or brown). N – Z – Any stone which is more yellow than N and less yellow than Z is called CAPE or DARK CAPE, Fancy Yellow – Any stone which is more yellow than Z is called Fancy Yellow and its value is higher.

sns.displot(data=data, x=data["color"], hue="cut", multiple="stack")

<seaborn.axisgrid.FacetGrid at 0x233cf204c40>

Count the diamonds according to their colors and according to the type of cut

sns.lineplot(data=data, x="carat", y="price")

<AxesSubplot:xlabel='carat', ylabel='price'>

There is a direct relationship between carat (weight of the diamond) and the price of diamonds